An investigation on the c-MYC, AXIN1, and COL11A1 gene expression in colorectal cancer.

The high incidence rate of CRC demands early diagnosis of the disease and readiness of diagnostic biomarker. In present study, we have investigated c-MYC, AXIN1, and COL11A1 expression levels in course of CRC progression and their correlation with demographics and clinical risk factors. Fifty-five tumors and 41 normal tissues were obtained from Tumor Bank of Iran, total RNA was extracted, cDNA was synthesized, and RT-qPCR was performed. Results were analyzed using Rest 2009 and SPSS software. Analysis at mRNA level showed upregulation of the two genes; c-MYC with a p-value of 0.001 and COL11A1 with an observed p-value of 0.02, while a p-value of 0.04 indicated AXIN1 downregulation. The observed overexpression of COL11A1 in stage 0 compared to other stages of CRC asserts importance of this gene in CRC prognosis. Moreover, statistical analysis confirms a significant correlation between expression of these genes and several clinical risk factors of CRC. Our study supports the importance of the studied genes and provides further information regarding the molecular mechanism of CRC. Further studies on these genes could elucidate their pivotal role for both early detection and/or diagnosis of CRC in addition to have important biomarkers for CRC management available.

Association of the Sp1 binding site and -1997 promoter variations in COL1A1 with osteoporosis risk: The application of meta-analysis and bioinformatics approaches offers a new perspective for future research.

As a complex disease, osteoporosis is influenced by several genetic markers. Many studies have examined the link between the Sp1 binding site +1245 G > T (rs1800012) and -1997 G > T (rs1107946) variations in the COL1A1 gene with osteoporosis risk. However, the findings of these studies have been contradictory; therefore, we performed a meta-analysis to aggregate additional information and obtain increased statistical power to more efficiently estimate this correlation. A meta-analysis was conducted with studies published between 1991-2020 that were identified by a systematic electronic search of the Scopus and Clarivate Analytics databases. Studies with bone mineral density (BMD) data and complete genotypes of the single-nucleotide variations (SNVs) for the overall and postmenopausal female population were included in this meta-analysis and analyzed using the R metaphor package. A relationship between rs1800012 and significantly decreased BMD values at the lumbar spine and femoral neck was found in individuals carrying the "ss" versus the "SS" genotype in the overall population according to a random effects model (p < 0.0001). Similar results were also found in the postmenopausal female population (p = 0.003 and 0.0002, respectively). Such findings might be an indication of increased osteoporosis risk in both studied groups in individuals with the "ss" genotype. Although no association was identified between the -1997 G > T and low BMD in the overall population, those individuals with the "GT" genotype showed a higher level of BMD than those with "GG" in the subgroup analysis (p = 0.007). To determine which transcription factor (TF) might bind to the -1997 G > T in COL1A1, 45 TFs were identified based on bioinformatics predictions. According to the GSE35958 microarray dataset, 16 of 45 TFs showed differential expression profiles in osteoporotic human mesenchymal stem cells relative to normal samples from elderly donors. By identifying candidate TFs for the -1997 G > T site, our study offers a new perspective for future research.

A preliminary computational outputs versus experimental results: Application of sTRAP, a biophysical tool for the analysis of SNPs of transcription factor-binding sites.

BACKGROUND: In the human genome, the transcription factors (TFs) and transcription factor-binding sites (TFBSs) network has a great regulatory function in the biological pathways. Such crosstalk might be affected by the single-nucleotide polymorphisms (SNPs), which could create or disrupt a TFBS, leading to either a disease or a phenotypic defect. Many computational resources have been introduced to predict the TFs binding variations due to SNPs inside TFBSs, sTRAP being one of them. METHODS: A literature review was performed and the experimental data for 18 TFBSs located in 12 genes was provided. The sequences of TFBS motifs were extracted using two different strategies; in the size similar with synthetic target sites used in the experimental techniques, and with 60 bp upstream and downstream of the SNPs. The sTRAP (http://trap.molgen.mpg.de/cgi-bin/trap_two_seq_form.cgi) was applied to compute the binding affinity scores of their cognate TFs in the context of reference and mutant sequences of TFBSs. The alternative bioinformatics model used in this study was regulatory analysis of variation in enhancers (RAVEN; http://www.cisreg.ca/cgi-bin/RAVEN/a). The bioinformatics outputs of our study were compared with experimental data, electrophoretic mobility shift assay (EMSA). RESULTS: In 6 out of 18 TFBSs in the following genes COL1A1, Hb cá´ª, TF, FIX, MBL2, NOS2A, the outputs of sTRAP were inconsistent with the results of EMSA. Furthermore, no p value of the difference between the two scores of binding affinity under the wild and mutant conditions of TFBSs was presented. Nor, were any criteria for preference or selection of any of the measurements of different matrices used for the same analysis. CONCLUSION: Our preliminary study indicated some paradoxical results between sTRAP and experimental data. However, to link the data of sTRAP to the biological functions, its optimization via experimental procedures with the integration of expanded data and applying several other bioinformatics tools might be required.

Analysis of microRNA and Gene Expression Profiles in Alzheimer's Disease: A Meta-Analysis Approach.

Understanding the molecular mechanisms underlying Alzheimer's disease (AD) is necessary for the diagnosis and treatment of this neurodegenerative disorder. It is therefore important to detect the most important genes and miRNAs, which are associated with molecular events, and studying their interactions for recognition of AD mechanisms. Here we focus on the genes and miRNAs expression profile, which we have detected the miRNA target genes involved in AD. These are the most quintessential to find the most important miRNA, to target genes and their important pathways. A total of 179 differentially expressed miRNAs (DEmiRs) and 1404 differentially expressed genes (DEGs) were obtained from a comprehensive meta-analysis. Also, regions specific genes with their molecular function in AD have been demonstrated. We then focused on miRNAs which regulated most genes in AD, alongside we analyzed their pathways. The miRNA-30a-5p and miRNA-335 elicited a major function in AD after analyzing the regulatory network, we showed they were the most regulatory miRNAs in the AD. In conclusion, we demonstrated the most important genes, miRNAs, miRNA-mRNA interactions and their related pathways in AD using Bioinformatics methods. Accordingly, our defined genes and miRNAs could be used for future molecular studies in the context of AD.